(1) Field of Invention
The present invention relates to a system for background estimation and, more particularly, to a system for background estimation which allows estimation of a stationary background mask of a dynamically changing view utilizing particle swarm optimization.
(2) Description of Related Art
Background estimation mechanisms have application in vision-based reconnaissance and security systems. These types of applications require accurate and fast object (i.e., people, cars, inanimate objects) detection capabilities and, therefore, would benefit greatly from reliable background estimation mechanisms as a way of initial detection and segmentation of foreground objects in a scene. Furthermore, a successful dynamic background estimation mechanism can greatly reduce the computational load of any machine vision-based object and event recognition system.
An increasing number of object tracking algorithms with active cameras have surfaced in recent literature. For instance, in “A Real-Time Tracking of Multiple Moving Object Contours in a Moving Camera Image Sequence” by S. Araki et. al in Transactions on Information and Systems, IEICE 2000, the authors propose algorithms which successfully track interested objects under various conditions. However, the object tracking algorithms fail to identify slow-moving or partially-moving objects, such as a stationary pedestrian waving his hands. These systems would greatly benefit from dynamic background estimation.
Additional prior art in background estimation and the use of background masks has been constrained primarily to stationary cameras, and often, to stationary backgrounds. Most prior art attempts to estimate the changing background by accumulating pixel information from a currently incident background requires accumulation of pixel information over several frames. During this background learning phase, the camera must remain stationary.
A few more sophisticated algorithms have recently surfaced which attempt to address small camera motions. In a recent paper entitled, “A Real-Time Background Subtraction Method with Camera Motion Compensation” by Tiehan Lv et al. in Proceedings of International Conference on Multimedia and Expo (ICME), 2004, the authors propose a background estimation and subtraction algorithm that is designed to work with a “shaking” camera. The method by Lv et al. relies on small incremental camera motion and accurate estimation of camera motion on the fly. Clearly, this and other methods are bound for failure in the case of wide-baseline camera movements.
It is well known in the art that solutions to challenging background estimation problems are fundamental to creation of next generation vision-based classification and tracking systems, particularly in moving camera settings. The present invention described herein is in response to present challenges in background estimation for moving cameras involving wide baseline displacements.